Bibliografías temáticas / Financial time series

Literatura académica sobre el tema "Financial time series"

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Publicado: 4 de junio de 2021
Última modificación: 14 de marzo de 2023

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Artículos de revistas sobre el tema "Financial time series"

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Politis, Dimitris N. "Financial time series". Wiley Interdisciplinary Reviews: Computational Statistics 1, n.º 2 (19 de agosto de 2009): 157–66. http://dx.doi.org/10.1002/wics.24.

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Dingli, Alexiei y Karl Sant Fournier. "Financial Time Series Forecasting – A Deep Learning Approach". International Journal of Machine Learning and Computing 7, n.º 5 (octubre de 2017): 118–22. http://dx.doi.org/10.18178/ijmlc.2017.7.5.632.

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Anderson, Gordon y Stephen Taylor. "Modelling Financial Time Series." Economic Journal 97, n.º 386 (junio de 1987): 512. http://dx.doi.org/10.2307/2232901.

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Ruiz, Esther y Lorenzo Pascual. "Bootstrapping Financial Time Series". Journal of Economic Surveys 16, n.º 3 (julio de 2002): 271–300. http://dx.doi.org/10.1111/1467-6419.00170.

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Gemmill, Gordon. "Modelling financial time series". International Journal of Forecasting 4, n.º 3 (enero de 1988): 496–97. http://dx.doi.org/10.1016/0169-2070(88)90115-x.

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Kinsella, A. y Stephen Taylor. "Modelling Financial Time Series." Statistician 36, n.º 4 (1987): 433. http://dx.doi.org/10.2307/2348865.

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Baillie, Richard T. "Modelling financial time series". European Journal of Operational Research 32, n.º 1 (octubre de 1987): 156–58. http://dx.doi.org/10.1016/0377-2217(87)90287-6.

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Taivan, Ariuna. "Financial Development And Economic Growth Revisited: Time Series Evidence". International Journal of Trade, Economics and Finance 9, n.º 3 (junio de 2018): 116–20. http://dx.doi.org/10.18178/ijtef.2018.9.3.599.

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Anderson, G. "Correction: Modelling Financial Time Series". Economic Journal 98, n.º 391 (junio de 1988): 566. http://dx.doi.org/10.2307/2233416.

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Audrino, Francesco. "Synchronizing multivariate financial time series". Journal of Risk 6, n.º 2 (febrero de 2004): 81–106. http://dx.doi.org/10.21314/jor.2004.105.

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Tesis sobre el tema "Financial time series"

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Yin, Jiang Ling. "Financial time series analysis". Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2492929.

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Ruiz, Ortega Esther. "Heteroscedasticity in financial time series". Thesis, London School of Economics and Political Science (University of London), 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308386.

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This thesis deals with two different topics, both related to modelling time-varying variances in high frequency financial time series. The first topic concerns the estimation of unobserved component models with autoregressive conditional heteroscedastic (ARCH) effects. The second topic concerns the quasi-maximum likelihood estimation of stochastic variance processes. These are an alternative to ARCH processes for modelling conditionally heteroscedastic time series. The motivation of the work is based on the increasing interest in the financial area in modelling volatility. In financial markets, many decisions are based on the volatility of a specific stock or index, which is closely related to the variance. Therefore, it is important to develop good statistical models able to describe time-varying variances. 2
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Buonocore, Riccardo Junior. "Complexity in financial time-series". Thesis, King's College London (University of London), 2018. https://kclpure.kcl.ac.uk/portal/en/theses/complexity-in-financial-timeseries(7c54cd37-fd3a-475b-83c1-539a55b4e3f9).html.

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Many aspects contribute to make financial markets one of the most challenging system to understand. The aim of this thesis is to study some aspects of their complexity by focusing on univariate e multivariate properties of log-returns time-series, namely multifractality and cross-dependence. In this thesis, we started by performing a thorough analysis of the scaling properties of synthetic time-series with different known scaling properties. This enabled us to do two things: find the presence of a strong bias in the estimation of the scaling exponents, and interpret measurement on real data which led us to uncover the true source of the multifractal behaviour of financial log-prices, which has been long debated in the literature. We addressed the presence of the bias by proposing a method which manages to filter out its presence and we validate it by applying it to synthetic time-series with known scaling properties and on empirical ones. We also found that this bias is due to the stability under aggregation of the log-returns which, due to their long memory, are processes which for high aggregation tend to a random variable which displays an exact multifractal scaling. Finally we focused the attention on linking the scaling properties of log-returns to their cross-correlation properties within a given market finding an intriguing non-linear relationship between the two quantities.
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Lee, Seonhwi. "Essays in financial time series". Thesis, University of Exeter, 2015. http://hdl.handle.net/10871/18569.

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This thesis consists of three essays on topics in financial time series with particular emphases on specification testing, structural breaks and long memory. The first essay develops an asymptotically valid specification testing framework for the Realised GARCH model of Hansen et al. (2012). The misspecification tests account for the joint dependence between return and the realised measure of volatility and thus extend the existing literature for testing the adequacy of GARCH models. The testing procedure is constructed based on the conditional moment principle and the first-order asymptotic theory. Our Monte Carlo results reveal good finite sample size and power properties. In the second essay, a Monte Carlo experiment is conducted to investigate the relative out-of-sample predictive ability of a class of conditional variance models when either a structural break or long memory is allowed. Our Monte Carlo results reveal that if the true volatility process is stationary short memory and its persistence level is not too high, but is contaminated by a structural break, the presence of the structural break is of importance in choosing a proper size of estimation window in the short-run forecast. If the persistence level is very high, spurious long memory may often dominate the true structural break in the longer-run forecast. For data generation processes without any structural break, the forecasting models, which can characterise the properties of the true conditional variance process, are favourable. In the last essay, we analyse the properties of the S&P 500 stock index return volatility process using historical and realised measures of volatility. We investigate a true property of the stochastic volatility processes by means of econometric tests, which may disentangle true or spurious long memory. The realised variance and realised kernel of the US stock market return exhibit true long memory. However, the historical volatility process shows some evidence of spurious long memory. We examine relative out-of-sample performance of one-day-ahead forecasts, with emphasis on the predictive content of structural changes and long memory. A class of ARFIMA models consistently produces the best-performing forecasts compared to a class of GARCH models. Among the GARCH models, it is shown that a rolling window GARCH forecast and GARCH forecasts which account for breaks outperform the long memory-based GARCH models even with the long memory proxy process.
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Ishida, Isao. "Essays on financial time series /". Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2004. http://wwwlib.umi.com/cr/ucsd/fullcit?p3153696.

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Mercurio, Danilo. "Adaptive estimation for financial time series". Doctoral thesis, [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=972597263.

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Yiu, Fu-keung y 饒富強. "Time series analysis of financial index". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31267804.

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Karanasos, Menelaos. "Essays on financial time series models". Thesis, Birkbeck (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286252.

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Dunne, Peter Gerard. "Essays in financial time-series analysis". Thesis, Queen's University Belfast, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337690.

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Schwill, Stephan. "Entropy analysis of financial time series". Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/entropy-analysis-of-financial-time-series(7e0c84fe-5d0b-41bc-96c6-5e41ffa5b8fe).html.

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This thesis applies entropy as a model independent measure to address research questions concerning the dynamics of various financial time series. The thesis consists of three main studies as presented in chapters 3, 4 and 5. Chapters 3 and 4 apply an entropy measure to conduct a bivariate analysis of drawdowns and drawups in foreign exchange rates. Chapter 5 investigates the dynamics of investment strategies of hedge funds using entropy of realised volatility in a conditioning model. In all three studies, methods from information theory are applied in novel ways to financial time series. As Information Theory and its central concept of entropy are not widely used in the economic sciences, a methodology chapter was therefore included in chapter 2 that gives an overview on the theoretical background and statistical features of the entropy measures used in the three main studies. In the first two studies the focus is on mutual information and transfer entropy. Both measures are used to identify dependencies between two exchange rates. The chosen measures generalise, in a well defined manner, correlation and Granger causality. A different entropy measure, the approximate entropy, is used in the third study to analyse the serial structure of S&P realised volatility. The study of drawdowns and drawups has so far been concentrated on their uni- variate characteristics. Encoding the drawdown information of a time series into a time series of discrete values, Chapter 3 uses entropy measures to analyse the correlation and cross correlations of drawdowns and drawups. The method to encode the drawdown information is explained and applied to daily and hourly EUR/USD and GBP/USD exchange rates from 2001 to 2012. For the daily series, we find evidence of dependence among the largest draws (i.e. 5% and 95% quantiles), but it is not as strong as the correlation between the daily returns of the same pair of FX rates. There is also dependence between lead/lagged values of these draws. Similar and stronger findings were found among the hourly data. We further use transfer entropy to examine the spill over and lead-lag information flow between drawup/drawdown of the two exchange rates. Such information flow is indeed detectable in both daily and hourly data. The amount of information transferred is considerably higher for the hourly than the daily data. Both daily and hourly series show clear evidence of information flowing from EUR/USD to GBP/USD and, slightly stronger, in the reverse direction. Robustness tests, using effective transfer entropy, show that the information measured is not due to noise. Chapter 4 uses state space models of volatility to investigate volatility spill overs between exchange rates. Our use of entropy related measures in the investigation of dependencies of two state space series is novel. A set of five daily exchange rates from emerging and developed economies against the dollar over the period 1999 to 2012 is used. We find that among the currency pairs, the co-movement of EUR/USD and CHF/USD volatility states show the strongest observed relationship. With the use of transfer entropy, we find evidence for information flows between the volatility state series of AUD, CAD and BRL.Chapter 5 uses the entropy of S&P realised volatility in detecting changes of volatility regime in order to re-examine the theme of market volatility timing of hedge funds. A one-factor model is used, conditioned on information about the entropy of market volatility, to measure the dynamic of hedge funds equity exposure. On a cross section of around 2500 hedge funds with a focus on the US equity markets we find that, over the period from 2000 to 2014, hedge funds adjust their exposure dynamically in response to changes in volatility regime. This adds to the literature on the volatility timing behaviour of hedge fund manager, but using entropy as a model independent measure of volatility regime. Finally, chapter 6 summarises and concludes with some suggestions for future research.
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Libros sobre el tema "Financial time series"

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Modelling financial time series. Chichester [West Sussex]: Wiley, 1986.

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Modelling financial time series. 2a ed. New Jersey: World Scientific, 2008.

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Tsay, Ruey S. Analysis of financial time series: Financial econometrics. New York: Wiley, 2002.

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Tsay, Ruey S. Analysis of Financial Time Series. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471746193.

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Mikosch, Thomas, Jens-Peter Kreiß, Richard A. Davis y Torben Gustav Andersen, eds. Handbook of Financial Time Series. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-71297-8.

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Tsay, Ruey S. Analysis of Financial Time Series. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470644560.

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Tsay, Ruey S. Analysis of Financial Time Series. New York: John Wiley & Sons, Ltd., 2005.

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Jens-Peter, Kreiß, Davis Richard A, Andersen Torben Gustav y SpringerLink (Online service), eds. Handbook of Financial Time Series. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.

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Analysis of financial time series. 2a ed. Hoboken, N.J: Wiley, 2005.

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Analysis of financial time series. 3a ed. Cambridge, Mass: Wiley, 2010.

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Capítulos de libros sobre el tema "Financial time series"

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Old, Oliver. "Financial time series". En Modeling Time-Varying Unconditional Variance by Means of a Free-Knot Spline-GARCH Model, 13–31. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-38618-4_2.

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Schmidt, Ruth A. y Helen Wright. "Time Series Analysis". En Financial Aspects of Marketing, 91–100. London: Macmillan Education UK, 1996. http://dx.doi.org/10.1007/978-1-349-25020-2_11.

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Basalto, Nicolas y Francesco De Carlo. "Clustering financial time series". En Practical Fruits of Econophysics, 252–56. Tokyo: Springer Tokyo, 2006. http://dx.doi.org/10.1007/4-431-28915-1_46.

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Borak, Szymon, Wolfgang Karl Härdle y Brenda López Cabrera. "Financial Time Series Models". En Statistics of Financial Markets, 123–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11134-1_11.

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Borak, Szymon, Wolfgang Karl Härdle y Brenda López-Cabrera. "Financial Time Series Models". En Universitext, 131–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33929-5_11.

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Gupta, Kartikay y Niladri Chatterjee. "Financial Time Series Clustering". En Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 2, 146–56. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63645-0_16.

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Aljandali, Abdulkader y Motasam Tatahi. "Time Series Analysis". En Economic and Financial Modelling with EViews, 37–55. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92985-9_3.

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Aljandali, Abdulkader y Motasam Tatahi. "Time Series Modelling". En Economic and Financial Modelling with EViews, 57–71. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92985-9_4.

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Borak, Szymon, Wolfgang Karl Härdle y Brenda López Cabrera. "ARIMA Time Series Models". En Statistics of Financial Markets, 135–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11134-1_12.

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Franke, Jürgen, Wolfgang Karl Härdle y Christian Matthias Hafner. "ARIMA Time Series Models". En Statistics of Financial Markets, 255–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16521-4_12.

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Actas de conferencias sobre el tema "Financial time series"

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Hong Zhang, Wenguo Li y Qiang Yu. "Multifractality of financial time series". En 2009 International Conference on Future BioMedical Information Engineering (FBIE). IEEE, 2009. http://dx.doi.org/10.1109/fbie.2009.5405890.

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He-Shan Guam y Qing-Shan Jiang. "Cluster financial time series for portfolio". En 2007 International Conference on Wavelet Analysis and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/icwapr.2007.4420788.

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CHENG, B. y H. TONG. "INTERVAL PREDICTION OF FINANCIAL TIME SERIES". En Proceedings of the Hong Kong International Workshop on Statistics in Finance. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2000. http://dx.doi.org/10.1142/9781848160156_0014.

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Lapenta, Evangelina S. y Sara M. Abecasis. "Identifying Nonlinearity in Financial Time Series". En 5. Congresso Brasileiro de Redes Neurais. CNRN, 2016. http://dx.doi.org/10.21528/cbrn2001-022.

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Tang, Kecheng y Bo Yuan. "Markov reconstruction of financial time series". En 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, 2013. http://dx.doi.org/10.1109/icicip.2013.6568112.

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Gopikrishnan, Parameswaran. "Financial time series: A physics perspective". En Third tohwa university international conference on statistical physics. AIP, 2000. http://dx.doi.org/10.1063/1.1291641.

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Lei, Su Te y Kang Zhang. "Visual signatures for financial time series". En the 2011 Visual Information Communication - International Symposium. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2016656.2016672.

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Mei, Xu y Huang Chao. "Financial time series difference analysis based on symbolic time series method". En 2011 International Conference on E-Business and E-Government (ICEE). IEEE, 2011. http://dx.doi.org/10.1109/icebeg.2011.5882598.

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Zhang, Hong, Haikun Zhou, Zhimin Liu y Keqiang Dong. "The Long-term Correlation of Conditional Time Series of Financial Time Series". En 2009 Third International Symposium on Intelligent Information Technology Application. IEEE, 2009. http://dx.doi.org/10.1109/iita.2009.124.

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Yaohui Bai, Jiancheng Sun, Jianguo Luo y Xiaobin Zhang. "Forecasting financial time series with ensemble learning". En 2010 International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS 2010). IEEE, 2010. http://dx.doi.org/10.1109/ispacs.2010.5704751.

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Informes sobre el tema "Financial time series"

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Bielinskyi, Andrii O., Serhii V. Hushko, Andriy V. Matviychuk, Oleksandr A. Serdyuk, Сергій Олексійович Семеріков, Володимир Миколайович Соловйов, Андрій Іванович Білінський, Андрій Вікторович Матвійчук y О. А. Сердюк. Irreversibility of financial time series: a case of crisis. Криворізький державний педагогічний університет, diciembre de 2021. http://dx.doi.org/10.31812/123456789/6975.

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The focus of this study to measure the varying irreversibility of stock markets. A fundamental idea of this study is that financial systems are complex and nonlinear systems that are presented to be non-Gaussian fractal and chaotic. Their complexity and different aspects of nonlinear properties, such as time irreversibility, vary over time and for a long-range of scales. Therefore, our work presents approaches to measure the complexity and irreversibility of the time series. To the presented methods we include Guzik’s index, Porta’s index, Costa’s index, based on complex networks measures, Multiscale time irreversibility index and based on permutation patterns measures. Our study presents that the corresponding measures can be used as indicators or indicator-precursors of crisis states in stock markets.
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Osipov, Gennadij Sergeevich, Natella Semenovna Vashakidze y Galina Viktorovna Filippova. Basics of forecasting financial time series based on NeuroXL Predictor. Постулат, 2017. http://dx.doi.org/10.18411/postulat-2017-7.

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Соловйов, Володимир Миколайович, V. Saptsin y D. Chabanenko. Markov chains applications to the financial-economic time series predictions. Transport and Telecommunication Institute, 2011. http://dx.doi.org/10.31812/0564/1189.

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In this research the technology of complex Markov chains is applied to predict financial time series. The main distinction of complex or high-order Markov Chains and simple first-order ones is the existing of after-effect or memory. The technology proposes prediction with the hierarchy of time discretization intervals and splicing procedure for the prediction results at the different frequency levels to the single prediction output time series. The hierarchy of time discretizations gives a possibility to use fractal properties of the given time series to make prediction on the different frequencies of the series. The prediction results for world’s stock market indices are presented.
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Soloviev, V., V. Saptsin y D. Chabanenko. Financial time series prediction with the technology of complex Markov chains. Брама-Україна, 2014. http://dx.doi.org/10.31812/0564/1305.

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In this research the technology of complex Markov chains, i.e. Markov chains with a memory is applied to forecast financial time-series. The main distinction of complex or high-order Markov Chains and simple first-ord yer ones is the existing of aftereffect or memory. The high-order Markov chains can be simplified to first-order ones by generalizing the states in Markov chains. Considering the «generalized state» as the sequence of states makes a possibility to model high-order Markov chains like first-order ones. The adaptive method of defining the states is proposed, it is concerned with the statistic properties of price returns.
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Соловйов, Володимир Миколайович, V. Saptsin y D. Chabanenko. Financial time series prediction with the technology of complex Markov chains. Transport and Telecommunication Institute, 2010. http://dx.doi.org/10.31812/0564/1145.

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In this research the technology of complex Markov chains, i.e. Markov chains with a memory is applied to forecast financial time-series. The main distinction of complex or high-order Markov chains [1] and simple first-order ones is the existing of after effect or memory. The high-order Markov chains can be simplified to first-order ones by generalizing the states in Markov chains. Considering the “generalized state” as the sequence of states makes a possibility to model high-order Markov chains like first-order ones. The adaptive method of defining the states is proposed, it is concerned with the statistic properties of price returns [2]. According to the fundamental principles of quantum measurement theories, the measurement procedure impacts not only on the result of the measurement, but also on the state of the measured system, and the behaviour of this system in the future remains undefined, despite of the precision of the measurement. This statement, in our opinion, is general and is true not only for physical systems, but to any complex systems [3].
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Соловйов, Володимир Миколайович, Vladimir Saptsin y Dmitry Chabanenko. Prediction of financial time series with the technology of high-order Markov chains. AGSOE, marzo de 2009. http://dx.doi.org/10.31812/0564/1131.

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In this research the technology of complex Markov chains, i.e. Markov chains with a memory is applied to forecast the financial time-series. The high-order Markov chains can be simplified to first-order ones by generalizing the states in Markov chains. Considering the *generalized state* as the sequence of states makes a possibility to model high-order Markov chains like first-order ones. The adaptive method of defining the states is proposed, it is concerned with the statistic properties of price returns. The algorithm of prediction includes the next steps: (1) Generate the hierarchical set of time discretizations; (2) Reducing the discretiza- tion of initial data and doing prediction at the every time-level (3) Recurrent conjunction of prediction series of different discretizations in a single time-series. The hierarchy of time discretizations gives a possibility to review long-memory properties of the series without increasing the order of the Markov chains, to make prediction on the different frequencies of the series. The technology is tested on several time-series, including: EUR/USD Forex course, the World’s indices, including Dow Jones, S&P 500, RTS, PFTS and other.
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Soloviev, Vladimir, Oleksandr Serdiuk, Serhiy Semerikov y Arnold Kiv. Recurrence plot-based analysis of financial-economic crashes. [б. в.], octubre de 2020. http://dx.doi.org/10.31812/123456789/4121.

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The article considers the possibility of analyzing the dynamics of changes in the characteristics of time series obtained on the basis of recurrence plots. The possibility of using the studied indicators to determine the presence of critical phenomena in economic systems is considered. Based on the analysis of economic time series of different nature, the suitability of the studied characteristics for the identification of critical phenomena is assessed. The description of recurrence diagrams and characteristics of time series that can be obtained on their basis is given. An analysis of seven characteristics of time series, including the coefficient of self-similarity, the coefficient of predictability, entropy, laminarity, is carried out. For the entropy characteristic, several options for its calculation are considered, each of which allows the one to get its own information about the state of the economic system. The possibility of using the studied characteristics as precursors of critical phenomena in economic systems is analyzed. We have demonstrated that the entropy analysis of financial time series in phase space reveals the characteristic recurrent properties of complex systems. The recurrence entropy methodology has several advantages compared to the traditional recurrence entropy defined in the literature, namely, the correct evaluation of the chaoticity level of the signal, the weak dependence on parameters. The characteristics were studied on the basis of daily values of the Dow Jones index for the period from 1990 to 2019 and daily values of oil prices for the period from 1987 to 2019. The behavior of recurrence entropy during critical phenomena in the stock markets of the USA, Germany and France was studied separately. As a result of the study, it was determined that delay time measure, determinism and laminarity can be used as indicators of critical phenomena. It turned out that recurrence entropy, unlike other entropy indicators of complexity, is an indicator and an early precursor of crisis phenomena. The ways of further research are outlined.
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8

Gomez-Gonzalez, Jose E., Jorge M. Uribe y Oscar M. Valencia. Risk Spillovers between Global Corporations and Latin American Sovereigns: Global Factors Matter. Inter-American Development Bank, mayo de 2022. http://dx.doi.org/10.18235/0004266.

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This paper studies volatility spillovers in credit default swaps (CDS) between the corporate sectors and Latin American countries. Daily data from October 14, 2006, to August 23, 2021, are employed. Spillovers are computed both for the raw data and for filtered series which factor out the effect of global common factors on the various CDS series. Results indicate that most spillovers occur within groups that is, within the series of sovereign CDS contracts and the price contracts of CDS issued by global corporations. However, considerable spillovers are also registered between LAC sovereigns and corporations. Interesting differences are encountered between filtered and unfiltered data. Specifically, spillovers from countries to corporations are overestimated (by about 4.3 percentage points) and spillovers from corporations to sovereigns are underestimated (by about 5.8 percentage points) when unfiltered data are used. This result calls for a revision of results obtained from studies that do not consider the role played by global common factors in system spillovers. Like in most related studies, spillovers show considerable time variation, being larger during times of financial or economic distress. When looking at total system spillovers over time, those corresponding to unfiltered series are always larger than those corresponding to filtered series. The difference between the two time series is largest in times of distress, indicating that global factors play a major role in times of crisis. Similar conclusions are derived from network analysis.
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9

Bielinskyi, Andrii O., Oleksandr A. Serdyuk, Сергій Олексійович Семеріков, Володимир Миколайович Соловйов, Андрій Іванович Білінський y О. А. Сердюк. Econophysics of cryptocurrency crashes: a systematic review. Криворізький державний педагогічний університет, diciembre de 2021. http://dx.doi.org/10.31812/123456789/6974.

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Cryptocurrencies refer to a type of digital asset that uses distributed ledger, or blockchain technology to enable a secure transaction. Like other financial assets, they show signs of complex systems built from a large number of nonlinearly interacting constituents, which exhibits collective behavior and, due to an exchange of energy or information with the environment, can easily modify its internal structure and patterns of activity. We review the econophysics analysis methods and models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. Quantitative measures of complexity have been proposed, classified, and adapted to the cryptocurrency market. Their behavior in the face of critical events and known cryptocurrency market crashes has been analyzed. It has been shown that most of these measures behave characteristically in the periods preceding the critical event. Therefore, it is possible to build indicators-precursors of crisis phenomena in the cryptocurrency market.
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Соловйов, В. М., В. В. Соловйова y Д. М. Чабаненко. Динаміка параметрів α-стійкого процесу Леві для розподілів прибутковостей фінансових часових рядів. ФО-П Ткачук О. В., 2014. http://dx.doi.org/10.31812/0564/1336.

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Modem market economy of any country cannot successfully behave without the existence of the effective financial market. In the conditions of growing financial market, it is necessary to use modern risk-management methods, which take non-gaussian distributions into consideration. It is known, that financial and economic time series return’s distributions demonstrate so-called «heavy tails», which interrupts the modeling o f these processes with classical statistical methods. One o f the models, that is able to describe processes with «heavy tails», are the а -stable Levi processes. They can slightly simulate the dynamics of the asset prices, because it consists o f two components: the Brownian motion component and jump component. In the current work the usage of model parameters estimation procedure is proposed, which is based on the characteristic functions and is applied for the moving window for the purpose of financial-economic system’ s state monitoring.
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